From incidental exposure to intentional avoidance: Psychological reactance to political communication during the 2017 German national election campaign

Abstract

This article deals with potential effects of incidental online exposure to political information (IOEP) on the German electorate during the 2017 national election campaign. We argue that the difference in currently unexpected and generally unwanted exposure to political online communication is crucial to the study of IOEP effects. While the former might result in “passive learning,” we hypothesize that–based on psychological reactance theory–the latter may be linked to defensive reactions with undesirable consequences among those who already are alienated from politics. Using cross-sectional data from an online survey among 1100 eligible voters, we can demonstrate that IOEP correlates with reactance in substantial segments. The study’s results are indicating, that the viral character of online campaigning may lead to the opposite of what was intended: voters are not only “trapped,” but might be repelled instead. Further empirical elaboration dealing with causal assumptions is encouraged.

Keywords

Campaign communication conditional effects inadvertent audience incidental exposure political online communication reactance theory

Introduction

The dissemination of political information and its impact on citizens have been a core interest within communication studies. In the second-half of the 20th century, it became apparent that, in addition to active news seeking, unintended exposure, that is, incidental exposure, had become a common form of exposure to political information (Blumler, 1970; Blumler and McQuail, 1968). The so-called “inadvertent audience” (Robinson, 1976: 426), who normally would not have watched news, became “trapped” while waiting for preferred TV programs. In this way, TV has exerted an almost universal influence on both interested and uninterested citizens’ political knowledge, opinions, and behavior (Schönbach and Weaver, 1985).

Contrary to the expectation that the inadvertent audience for political information would vanish due to more “self-reflective,” segmented audiences in the age of digital media (Bennett and Iyengar, 2008: 718), Tewksbury et al. (2001) showed in their research that about half of American Internet users receive unintended political information while being online for other reasons (p. 540). Later, similar conclusions were arrived at for different countries concerning various online platforms (e.g. Flemming and Marcinkowski, 2016; Fletcher and Nielsen, 2018; Kim et al., 2013b; Mitchell et al., 2016).

The rise of social media led to a significant increase in research on incidental online exposure to political information (IOEP). However, uncertainties exist in research evidence on the effects of IOEP. While some studies find positive effects on knowledge and political participation (e.g. Tewksbury et al., 2001; Valeriani and Vaccari, 2016), others find no associations or even negative ones (e.g. Heiss et al., 2019; Oeldorf-Hirsch, 2018). Furthermore, political interest repeatedly has been shown to be a precondition, rather than one of IOEP’s consequences (Flemming and Marcinkowski, 2016; Tewksbury et al., 2001). This raises doubts about the assumption that IOEP is, indeed, a “political information trap” for politically unsophisticated Internet users.

This article deals with IOEP’s effects on different segments of the German electorate during the 2017 national election campaign. In this study, we assume that different groups of voters might react to IOEP in different ways with different effects. The crucial point is whether and to what extent a voter is inured to informing himself about politics and the election. More precisely, we suspect that IOEP sometimes will happen to eligible citizens who basically are accustomed to getting their political information from the web, along with other sources, but who get online to do something else (e.g. play games, shop, watch a YouTube video, etc.). These voters will be surprised by unexpected incoming messages and may even feel disturbed, but they will not respond in a generally negative way. Under these conditions, passive learning (Krugman and Hartley, 1970; Zukin and Snyder, 1984) from IOEP may occur if a user is willing to be distracted from his or her original intention and to pursue the unexpected cue. We refer to this IOEP mode as currently unexpected exposure. However, other groups of voters will lack the necessary minimum of political interest. Accordingly, they will perceive unexpected contact with political content as a disruption of their actual intentions, feel restricted in their behavioral freedom, and become annoyed. We suggest, for purposes of this IOEP mode, that the term generally unwanted exposure to politics and the election be used. Under these conditions, based on psychological reactance (e.g. Dillard and Shen, 2005; for particular assumptions in the context of IOEP see also Heiss and Matthes, 2019: 15), we expect not only no learning effects, but also more far-reaching, defensive reactions with democratically undesirable consequences, for example, intensified avoidance of political, and particularly campaign-related, information.

This study contributes to extant literature on IOEP in two ways: First, it introduces unexpected and unwanted exposure to political information online as two different patterns of IOEP by combining two existing measures in a novel way. Second, based on psychological reactance theory, we demonstrate that for some IOEP is not only disconnected from political knowledge (a commonly used indicator for a desirable effect), but actually correlates with intentional avoidance of political information. This applies in particular to users who are already alienated from the political process.

Literature review of incidental online exposure research

Using survey data from 1996 to 1998, Tewksbury et al. (2001) were among the first to hint at a significant portion of Internet users encountering political information incidentally as a byproduct of other online activities. For only one out of three elections under investigation, the authors were able to show a small, but significant, positive effect from IOEP on current-affairs knowledge. Using various methods, the knowledge-gain hypothesis in later studies was either supported (Bode, 2016; Lee and Kim, 2017; Morris and Morris, 2017) or rejected because no link between IOEP and knowledge could be found (Oeldorf-Hirsch, 2018). Some studies even hinted at a negative effect (Flemming and Marcinkowski, 2016)—at least for those who rely on being informed only incidentally (Gil de Zúñiga et al., 2017). However, both positive and negative effects on political knowledge are generally rather small.

Mixed results also can be observed with other dependent variables, such as political participation. While Valeriani and Vaccari (2016) found that frequent IOEP increases political participation behavior, Flemming and Marcinkowski (2016) showed that political mobilization occurs only among the politically interested. Kim et al. (2013b) found mobilizing effects from IOEP on online and offline participation, but only among users with surveillance motivation. Heiss et al. (2019) demonstrated a small, but negative, effect from IOEP on offline political engagement, whereas intentional news exposure mobilized strongly. A replication of Valeriani and Vaccari’s (2016) study, which is based on panel data, shows that the participatory gap is increasing even as politically interested individuals are mobilized, but the effect of incidental exposure on participation for those with low political interest is negative (Heiss and Matthes, 2019).

Yadamsuren and Erdelez (2010) and Yadamsuren and Heinström (2011) focused on affective reactions to IOEP. They concluded that positive emotions could occur as a consequence of opinion consistency and general interest in political information. However, negative emotions also were mentioned in relation to incidental exposure. Recipients described such negative emotions from this “distraction” and a feeling that it was a “waste of time” (Yadamsuren and Heinström, 2011: 13).

While traditionally, IOEP research has argued that passive learning and mobilization may occur, the results presented thus far challenge this generally optimistic view. Depending on respected controls and study designs, democratically desirable effects are not the only ones elicited from IOEP. Some studies even point to potential challenges to democratic values due to widening knowledge and participation gaps—even leading to negative affective reactions. Thus, a need exists to understand who the inadvertent audience is and how incidentally received information is processed.

Spending more time on the Internet has been shown to be one of the main drivers of IOEP (Flemming and Marcinkowski, 2016; Lee and Kim, 2017; Tewksbury et al., 2001). The same applies to people having more diverse social networks online, either due to network heterogeneity or a bigger share of weak ties, entailing people who are not part of other people’s everyday lives (Ahmadi and Wohn, 2018; Lee and Kim, 2017). Both more online time and diverse online social networks increase the chances of exposure to news that was not sought intentionally and might even contradict one’s own positions.

Even though Boczkowski et al. (2018) find that incidental news exposure also might happen during leisure time, the supposed counterpart, intentional news exposure, is highly correlated with IOEP as well (Ahmadi and Wohn, 2018; Ardèvol-Abreu et al., 2017; Boczkowski et al., 2018; Tewksbury et al., 2001). As previously said, actively browsing news pages or reading news obviously includes coming across unknown or unsought political information more often. Furthermore, those who are politically interested (Flemming and Marcinkowski, 2016; Tewksbury et al., 2001) or use the web for political discussions (Lee and Kim, 2017) report higher frequency of IOEP. In the same vein as intentional news seeking, the effects from political interest and frequent discussions indicate that political sophistication seems to be a predictor of IOEP frequency.

However, as Kaiser et al. (2018: 3) put it, incidental contact and incidental exposure are not exactly identical. Most extant studies that have examined antecedents of IOEP focus on contact when talking about incidental exposure. Only a few have focused on the actual processing of incidentally received information, even though not every contact must result in an elaborate processing of that same information. While IOEP may engage people in processing news in some way (Oeldorf-Hirsch, 2018), the intensity is higher for news that is actively sought (Boczkowski et al., 2018). Furthermore, being interested in the topic at stake is central to engagement level (Karnowski et al., 2017; Kümpel, 2019; Mitchell and Page, 2013). Thus, not only is the chance to receive political information accidentally dependent on political sophistication, but also the ability and willingness to process this information. Consequently, effects such as knowledge gain and mobilization can be expected to be more likely for those whose interest already has turned toward politics.

A differential approach to incidental exposure

Considering all that has been mentioned so far, indications exist that democratically desirable IOEP effects observed in the aggregate cannot be expected equally for all potential recipients. Apart from the character of those who encounter news accidentally and the way they process this information, researchers differ in the way they operationalize IOEP. Most studies, at least those using quantitative survey designs, measure self-reported frequency of IOEP.¹ While Tewksbury et al. (2001) only asked respondents whether they experienced IOEP while they were online, subsequent studies analyze how often this happens to respondents—either in general while online (Valeriani and Vaccari, 2016) or concerning different online outlets in particular (Ardèvol-Abreu et al., 2017; Flemming and Marcinkowski, 2016; Kim et al., 2013b; Oeldorf-Hirsch, 2018).² While measuring self-reported experiences with IOEP, these studies focus on one single dimension, namely frequency of occurrence. Thus, they are limited to the incident itself, while not providing anything about its evaluation from the person concerned.

Asking specifically for the way that respondents usually are exposed to political information online, Heiss et al. (2019), as well as Ahmadi and Wohn (2018), distinguish between incidental and intentional exposure by using a separate measure for each mode. Their scales indicate how much a respondent is generally (or is not) inclined to search actively for political information online. This does not mean that respondents cannot be reached unintentionally, but that—as Heiss et al. show (2019: 13)—a deliberate motivation to look for political information is rather absent. It might, therefore, be assumed that a high value on the incidental exposure mode (IEM) scale indicates a certain level of resistance to political information. However, both studies do not consider frequency of exposure and remain with this general exposure mode as an indicator of recipients’ overall approach to political information. Whether and how often IOEP happens to them is not examined.

We suggest using a combination of both measures, as both dimensions gain full explanatory power if each is taken into account. On the one hand, being exposed to political information accidentally is only a distraction when disliking this exposure concurrently. On the other hand, it seems unlikely that positive effects may occur when any interest in politics is absent. Thus, this study will connect both established measures—IEM as a resistance indicator and self-reports of IOEP as a frequency measure. The product of frequency and resistance indicates the number of occasions for annoyance. Thus, we can differentiate between unexpected and unwanted exposure. In the following section, we are interested mostly in the supposed effect of the latter modus, namely psychological reactance.

Incidental online exposure to politics and psychological reactance

The Internet always has been associated to the idea of freedom, particularly with free choice of information and communicative actions (e.g. Bennett and Iyengar, 2008). Following this idea, Internet users should believe, at the very least, in the ability to choose what and how much information they want to receive while online. IOEP, as a frequently experienced phenomenon, principally contradicts this expectation. In particular, when usually not searching for political information or even intentionally trying to avoid the same, frequent IOEP could be perceived as a threat to one’s free decision-making on how much and what information one aims to receive. This may already succeed well during routine periods. Due to various curation practices, for example on social networking sites, only those providers can be followed who provide information of interest. However, this seems to be more difficult for election campaign times. Not only is professional communication by political actors (Ceccobelli, 2018) and political reporting by journalists changing (Van Aelst and De Swert, 2009; Walgrave and Van Aelst, 2006), but ordinary citizens are also expressing themselves more intensively and polarizingly about politics and the election campaign in online media (Hosch-Dayican et al., 2016). In this way, for example, political news items, election campaign videos or get-out-the-vote calls spread virally, and despite all efforts it seems that it is no longer possible to avoid (unwanted) election campaign information. Especially when either not at all interested in the elections, being cynical about politics and counter-attitudinal positions, or just using the Internet, for example, for entertainment purposes, one might feel forced to deal with unwanted political information (see also Heiss and Matthes, 2019). Psychological reactance theory suggests that this so-called threat to freedom could arouse a motivational state of reactance that drives behaviors to restore the lost freedom of behavior (Brehm, 1966; Brehm and Brehm, 1981; Dillard and Shen, 2005; Miron and Brehm, 2006; Wicklund, 1974).

Dillard and Shen (2005) conceptualize freedom of behavior as the free choice of performing particular actions while being aware of alternatives. Subsequently, threat to freedom is the subjective individual perception that someone or something might limit one’s free choice of what to do, making it difficult to perform the preferred action autonomously (Dillard and Shen, 2005: 145). As shown in various studies, restricting messages and situations that force a particular behavior or prevent a preferred one increase the level of perceived threat to freedom (e.g. Andreoli et al., 1974; Dillard and Shen, 2005; Katz et al., 2017).

As a reaction to that threatening situation, reactance, as a psychological construct, has been defined as a motivational state (Brehm and Brehm, 1981; Steindl et al., 2015) that is reactive and not proactive (Miron and Brehm, 2006). Thus, an individual first must encounter a freedom-threatening situation before reactance occurs. When talking about a motivational state, reactance is understood as a driver of future actions related to endangered freedom. While Brehm and Brehm (1981) argued that reactance is an “intervening, hypothetical variable” (p. 37) that helps with understanding behavioral reactions to threats against freedom and is used rather as a theoretical explanation, Dillard and Shen (2005) criticized the authors “reluctance to provide greater conceptual explication of the principal mechanism” (p. 146). Reviewing theoretical and empirical literature, they considered (a) a purely cognitive, (b) a purely affective, (c) a dual process, and (d) an intertwined understanding of a cognitive and affective component of reactance (Dillard and Shen, 2005: 146–147). In an empirical study, the authors aimed to test the validity of the four conceptualizations of reactance. Using confirmatory factor analysis (CFA), they demonstrate that an intertwined process model of reactance had the best model fit; thus, it can be assumed that affective and cognitive reactions can barely be disentangled when thinking about reactance (Dillard and Shen, 2005: 157). Moreover, further research has strongly supported their findings (e.g. Kim et al., 2013a; Quick, 2012; Quick and Stephenson, 2007; Rains, 2013), establishing their conceptualization of reactance.

Finally, reactance as a motivational state is directed to restoration actions, which describe behaviors that aim to restore lost or threatened behavioral freedom (Brehm, 1966; Dillard and Shen, 2005; Miron and Brehm, 2006; Steindl et al., 2015). This may happen directly when “doing the forbidden act” (Dillard and Shen, 2005: 145) or indirectly by changing perceptions, attitudes, preferences, or even ignoring the threat (e.g. Bessarabova et al., 2013; Meirick and Nisbett, 2011; Silvia, 2006; Worchel and Brehm, 1971).

Applying this theoretical concept to IOEP (see Figure 1), we first focus on the individual’s freedom online. Assuming that Internet users especially appreciate their free choice of information and actions online, being exposed inadvertently to political information might be perceived as a threat to this particular freedom. However, the level of perceived threat is dependent on both the general readiness for political information and the actual frequency of IOEP. While those who usually are interested in politics and just stumble upon political information by accident might appreciate (or at least accept) this exposure, those lacking any inclination toward political information may be highly disturbed by IOEP. Thus, we define the threat of freedom to be higher when involuntarily exposed to political information. Unwanted exposure arises from frequent exposure to respective information, albeit under general resistance regarding intentional exposure to political information. Following reactance theory, if unwanted exposure, as a threat to free choice of information, occurs, we expect these recipients to react with more reactance to that threat. Thus, we propose these hypotheses:

H1a: The frequency of incidental online exposure to campaign communication is positively associated with psychological reactance among voters who are reluctant to inform themselves voluntarily about politics and the election (unwanted exposure).

H1b: The frequency of incidental online exposure to campaign communication, is not predictive for psychological reactance among voters who are inclined to inform themselves voluntarily about the election occasionally (unexpected exposure).

Figure 1.

Theoretical model, including hypotheses for unwanted exposure’s effect on reactance and information avoidance.

Reactance theory predicts behavioral reactions, that is, so-called restoration actions, as a consequence of a perceived freedom-threatening situation. Since unwanted exposure is defined as being exposed to information on the elections while usually reluctant when faced with intentional exposure, the restoration action must be defined as further improvement in news avoidance strategies. In a manner of a “now more than ever” motivation, we expect people being involuntarily exposed to campaign news to react with even stronger intentions to avoid exposure to political information. Among others, Dillard and Shen (2005) showed that restoration acts follow psychological reactance. Therefore, we expect reactance to mediate the effect of IOEP frequency in avoiding campaign news consumption:

H2: Higher psychological reactance toward unwanted exposure to campaign communication online is associated positively with intentional avoidance of additional information on the elections.

H3: The frequency of unwanted exposure is associated positively with intentional avoidance of additional information on the elections. This relation is mediated by psychological reactance.

As mentioned earlier, knowledge acquisition is one of the central concepts of previous IOEP research. So far, it is not clear how such cognitive effects are to be understood in the light of the distinction between unexpected and unwanted exposure to political information online. If one follows the frequently used concept of passive learning to explain knowledge gain through incidental contact with media content, the effect results precisely from the fact that no cognitive resistance opposes information processing from casual, effortless reception (Krugman and Hartley, 1970: 188). However, under the conditions of reactance, this is precisely the case: Because the recipient feels restricted and disturbed, he or she is not only emotionally irritated, but also cognitively prepared to be defensive. Therefore, it is to be expected that passive learning is not a general result of incidental exposure to media content, but will occur only to those who do not react because they merely feel surprised, but not disturbed. Thus, knowledge acquisition might be a result of unexpected, but not unwanted, exposure:

H4: Frequent IOEP to election campaign communication predicts knowledge acquisition for those who do not oppose voluntary election information (unexpected exposure; H4a) and does not predict knowledge acquisition for those who oppose voluntary election information (unwanted exposure, H4b).

Method

To test our hypotheses, we conducted an online survey among German Internet users in the course of the 2017 national election campaign. Although our hypotheses imply an experimental approach, we rely on self-reporting of previously experienced media use to keep external validity high. Knowing that causality might be an issue, this limitation will be considered in the “Discussion” section.

The sample was drawn from the Respondi online access panel.³ The study was conducted in Germany from 11 to 18 September 2017, 2 weeks prior to the general elections. A quota sample was chosen to get a close fit between characteristics of the German online population and the sample (quota were defined based on age and gender). Altogether, 2552 invitations were sent out, 1231 clicks were registered, and 1102 respondents finished the questionnaire. The response rate based on American Association for Public Opinion Research’s (AAPOR, 2016) RR1 standard is RR1 = .431. After cleaning responses with too many missing answers and overly fast responses, the data set contained 1001 respondents. The remaining sample still fit the quotation specifications for German Internet users quite well (Table 1).⁴

Table 1.

Gender and age quota and sample description (N = 1001).

	Actual sample (%)	AGOF^b 2015 (%)
Male	53.2	52.2
Female	46.8	47.8
18–29 years	19.4	19.5^a
30–39 years	17.6	18.8
40–49 years	24.4	24.0
50–59 years	19.7	19.6
60+ years	19.0	18.1
	Age: M = 45.18; SD = 15.27;Range: 18–88 years

SD: standard deviation.

AGOF Internet facts study considers only 20- to 29-year-olds; the base are all Internet users age 20 years and older.

AGOF: Arbeitsgemeins Online forschung.

Measures

Independent variable: unwanted online exposure to election campaign

As previously said, we consider frequent IOEP, particularly when the person usually is uninterested when faced with political information, as an indicator of unwanted exposure and, thus, as a perceived threat to behavioral freedom. Two distinct measures are needed.⁵

First, building on previous work by Kim et al.’s (2013b) extended measures of self-reported frequency of incidental news exposure, we adapted their scale to our study and asked for seven different outlets: “When online on [SPECIFIC PLATFORM], how often do you happen to learn something about the upcoming national elections, although you are on the Internet for other reasons?” (1 = never, 5 = almost always; seven-item averaged scale; Cronbach’s α = .82; M = 2.45; SD = 1.00). Since 115 respondents never experienced IOEP during the national election season in 2017 and, thus, could not have been asked for cognitive and affective reactions to IOEP, the following analysis is based on 886 respondents who faced at least some incidental exposure (88.5% of all cases).

Second, to distinguish unexpected from unwanted exposure, we used the suggested scale by Heiss et al. (2019) to measure the so-called IEM. As already suggested earlier, this measurement indicates to some extent the absence of any motivation to engage deliberately with political information. From this, we conclude that it is a suitable indicator of resistance to intentional exposure to campaign communication. Respondents were asked whether they agreed that they usually: (a) stumble across information on the general elections only by accident, (b) only see information on the general elections when other people from their network post about politics, or (c) do not seek information on the general elections, but sometimes see that information by accident (seven-point response scale; three-item averaged scale; Cronbach’s α = .74; M = 3.59; SD = 1.43). Since IEM functions as a moderator in our theoretical model, we employed the most straightforward of several suggestions for latent interaction analysis using structural equation modeling (SEM; Ping, 1995; Steinmetz et al., 2011), namely calculating the same model in different subgroups of the initial sample by performing Multi-Group SEM. Subgroups are defined using a median split of IEM (Median = 3.67) and dividing the sample into a low IEM group (IEM ⩽ 3.67; n = 456) and a high IEM group (IEM > 3.7; n = 430). While members of the first group experience frequent IOEP as simply unexpected exposure, members of the latter group view IOEP as a rather unwanted experience.

Mediator: election campaign reactance

As suggested by Dillard and Shen’s (2005) conceptualization of reactance, we consider affective and cognitive reactions as two intertwined subdimensions of reactance. In line with the authors, we include reactance as a second-order latent factor in the model. Frustration and cognitive obstinacy are represented as first-order latent factors, with four items each.

The affective component of psychological reactance was operationalized as frustration (Blacha, 2014; Gelbrich, 2010). Respondents were asked to indicate how they felt (e.g. angry, frustrated, annoyed, upset) when being incidentally confronted with information on the elections on a seven-point response scale. Similar measures have been used by Dillard et al. (Dillard and Peck, 2000; Dillard and Shen, 2005).

Instead of the open-ended question for counter-arguing, as originally used by Dillard and Shen, we chose an adaptation of the close-ended battery of state reactance suggested by Hong and Page (1989; adaptation and short scale by Sachse, 2010), asking for respondents’ agreement on four items (e.g. “I resist the attempts of electoral campaigns to influence me”; five-point response scale). This measurement is an indicator for cognitive reactions toward the perceived threat to behavioral freedom.

A CFA was conducted to test the factors for internal consistency. Due to better model fit for both frustration and cognition, one item was removed, and the CFA was repeated. Furthermore, to ensure measurement invariance between groups, the measurement models had to be tested for potential spillover effects from the moderator on reactance. Thus, we conducted a model comparison for reactance between the two exposure groups. Constraining both first- and second-order measurement weights, the measurement model of reactance—comprising frustration and cognitive obstinacy—fits the data quite well, indicating that the reliability of our reactance measurement is good enough for further analysis (N = 886; χ²[18]= 15.254; p = .644; SRMR = .015; RMSEA = .000 [.000; .025]; CFI = 1.000; TLI = 1.000). No significant improvement could be achieved by setting measurements free (N = 886; χ²[4]= 1.611; p = .807).

Dependent variable: information avoidance and current campaign knowledge

Finally, the dependent variable is the restoration act, that is, a behavior to restore threatened or limited behavioral freedom. The freedom in question is the opportunity to choose voluntarily what information one is searching for and, thus, also what information one tries to avoid. We use a measure representing political information avoidance, particularly avoidance of information on Germany’s 2017 national elections. Metag and Arlt (2017) introduced and tested an information-avoidance scale, which we adapted to the special case of national elections. We asked respondents to indicate their agreement or disagreement with three statements on a five-point response scale, noting their intention to keep away from electoral information (“I dodge the election campaigns wherever possible”; “I try to switch the topic of discussion when turning toward the election campaigns”; “I barely scan print and online news on the election campaigns”). Model fit indices indicate a good internal consistency for information avoidance (N = 886; χ²[3]= 1.140; p = .768; SRMR = .006; RMSEA = .000 [.000; .038]; CFI = 1.000; TLI = 1.000). Again, the requirement of measurement invariance between the two groups is met (N = 886; χ²[1] = .237; p = .626).

Current campaign knowledge is based on 11 questions dealing with events related to the campaign, candidates, and party programs. A requirement was this information to be disseminated most notably on social media or online news sites (e.g. information shared in YouTube interviews). For each question, three possible answers were provided, with one true and two false statements. Participants had the option of refusing to answer. False and refused answers were coded as 0; correct answers were coded as 1. Subsequently, the sum of all 11 questions was leading to a 12-step scale of current campaign knowledge (0–11; M = 4.35; SD = 2.35; please find the wording of questions in Supplemental Appendix A).

Control variables

A set of additional variables that have been found to be related to (political) information avoidance was included in the models as well as gender (54% male), age (M = 44.8; SD = 15.4) and formal education (three-point scale; M = 2.14; SD = .61). As was shown earlier, political interest is a predictor of both incidental and intentional news exposure. According to the European Social Survey (ESS, 2016), we asked the respondents to indicate their political interest on a four-point response scale (1 = “not at all interested” to 4 = “very much interested”) for three geographical dimensions (“politics in Germany,” “politics in European countries,” “politics in other countries out of [Region]”; Cronbach’s α = .88; M = 2.74; SD = .74). Furthermore, research on the spiral of cynicism argues that political cynicism and media use are related to each other (Aarts and Semetko, 2003; Cappella and Jamieson, 1997; Valentino et al., 2001), even though it is unclear whether cynicism increases or decreases information acquisition (De Vreese, 2005). Still, we consider political cynicism to be a relevant control in our study, as we included the established scale by Pinkleton and Austin (2001), which measures political cynicism with four items on a five-point response scale (Cronbach’s α = .92; M = 3.91; SD = .98). Satisfaction with democracy was measured by using the standard question from the ESS 2016 (“And on the whole, how satisfied are you with the way democracy works in Germany?”). Answers were provided on a five-point response scale (M = 3.27; SD = 1.12). And finally, proven as an essential antecedent of IOEP, general Internet use was also included to the model, measured with an approximated average amount of minutes per day (M = 123.0; SD = 119.9).

Data analysis

To test our hypotheses, we employed SEM using AMOS 23. Full information maximum likelihood was used as an estimator. All variables, except the controls and the independent variable, were entered as latent constructs. Self-reported frequency of incidental exposure, political interest and political cynicism was parceled, and satisfaction with democracy was represented by one manifest variable in the data set. All latent variables and controls’ means, standard errors, and zero-order correlations are shown in Table 2 (please find covariance matrix for all variables in Supplemental Appendix B). Model fit is reported based on all relevant fit indices (Holbert and Stephenson, 2002; Hu and Bentler, 1999) in Figure 2 (please find the complete model including all controls and factor loadings in Supplemental Appendix C).

Table 2.

Implied mean, standard error, and zero-order correlation matrix of study variables.

	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13
Group 1: Respondents experiencing IOEP as unexpected exposure to campaign information (IEM > 3.67; n = 430)
1. Gender	.51	.02	–
2. Age	43.95	.73	−.041	–
3. Education	2.14	.03	.012	−.108*	–
4. Gen. Internet use	127.83	6.0	.051	−.040	−.156**	–
5. Pol. interest	2.68	.04	.149**	.110*	.091	−.020	–
6. Pol. cynicism	4.00	.04	−.059	.283**	−.060	.026	−.069**	–
7. Demo. satisfaction	3.22	.05	.038	−.132*	.098*	−.023	.175	−.375**	–
8. Frequency IOEP	2.69	.05	.007	−.216**	.022	.026	.271**	−.015	.150**	–
9. Reactance	2.47	.43	.071	.176**	−.015	−.023	−.070	.488**	−.244**	.139*	–
10. Frustration	3.10	.60	.051	.125**	−.011	−.017	−.049	.347**	−.173**	.098*	.711**	–
11. Cog. obstinacy	2.47	.43	.067	.165**	−.014	−.022	−.065	.458**	−.228**	.130*	.938**	.666**	–
12. Info avoidance	.98	.40	.022	.093	−.001	−.044	−.300**	.331**	−.110*	−.030	.716**	.509**	.671**	–
13. Current campaign knowledge	3.98	.11	.253**	.200**	.140**	.030	.357**	.015	.172**	.048	−.083	−.059	−.078	−.215**	–
Group 2: Respondents experiencing IOEP as unwanted exposure to campaign information (IEM ⩽ 3.67; n = 456)
1. Gender	.56	.02	–
2. Age	45.55	.72	−.008	–
3. Education	2.15	.03	.020	−.157**	–
4. Gen. Internet use	118.43	5.3	.060	−.074	−.061	–
5. Pol. interest	2.85	.03	.143**	.131**	.182**	−.061	–
6. Pol. cynicism	3.81	.05	−.019	.244**	−.089	.140**	−.116**	–
7. Demo. satisfaction	3.32	.05	.046	.000	.035	−.055	.183**	−.463**	–
8. Frequency IOEP	2.55	.04	−.042	.037	−.007	.062	.147**	.018	−.044	–
9. Reactance	−.65	.42	−.046	.160*	−.181**	.090	−.331**	.473**	−.378**	−.137*	–
10. Frustration	−.65	.43	−.029	.100*	−.113**	.056	−.207**	.297**	−.237**	−.086*	.627**	–
11. Cog. obstinacy	−.65	.42	−.045	.155	−.176	.087	−.321	.459	−.367	−.133*	.970	.608	–
12. Info avoidance	−.53	.38	−.091	.163**	−.040	.030	−.400**	.358**	−.255**	−.202**	.769**	.482**	.746**	–
13. Current campaign knowledge	4.71	.11	.236**	.137**	.191**	−.020	.446**	−.009	.112*	.150**	−.242**	−.152**	−.234**	−.238**	–

IEM: incidental exposure mode; IOEP: incidental online exposure to political information; SD: standard deviation.

Note. Bootstrap estimation of Standard Errors with 1000 samples.

Two-tailed significance.*p < .05;**p < .01.

Figure 2.

Structural equation model for respondents experiencing IOEP as (a) unwanted exposure (n = 430; IEM > 3.67) and (b) unexpected exposure (n = 456; IEM ⩽ 3.67).

Results

To differentiate between unexpected and unwanted exposure, the SEM was calculated for both exposure groups. Figure 2(a) shows the model for respondents experiencing IOEP as rather unwanted exposure. For this group, IOEP arguably represents a threat to behavioral freedom. Figure 2(b) shows the same model, but for respondents experiencing IOEP as unexpected exposure. This group is inclined to use political information voluntarily at least occasionally.

In H1, we expected that higher frequency of unwanted contact would be linked to greater reactance (H1a), while there would be no relation with the frequency of IOEP as unexpected contact (H1b). The data support our assumptions. In line with H1a, for respondents experiencing IOEP as unwanted exposure, the correlation of self-reported frequency with psychological reactance is ß = .213, indicating that the more a person experiences IOEP while generally being resistant to political information, the higher the psychological reactance. However, for respondents experiencing IOEP as unexpected exposure, a negative correlation exists between frequency of IOEP and reactance (ß = −.123). Unexpected exposure not only seems to be accepted, but also appreciated. The chi-square difference test indicates that both groups differ significantly regarding the association of self-reported frequency of IOEP and reactance (χ²[1]= 23.918; p < .01).

Furthermore, H2 predicted that in reaction to psychological reactance, a restoration act will follow, namely the intention to avoid political information. Generally, in both models, reactance is correlated highly with avoidance of election news as an attempt to restore the freedom of choosing what information is relevant and wanted. Thus, our data strongly support H2. Furthermore, a chi-square difference test reveals no difference between the two groups concerning this path (χ²[1]= 1.648; p = .977). Thus, the association of psychological reactance with avoidance of political information is independent of the general resistance to political information.

In H3, we expected unwanted exposure to be positively related to information avoidance mediated by psychological reactance. Thus, for H3, we are interested in both direct⁶ and indirect connections (Table 3). For respondents experiencing IOEP as unwanted exposure, total and direct effects do not reach significance. However, the indirect connection is highly significant (ß = .157), supporting the mediation hypothesis, that is, a higher frequency of unwanted exposure is associated with higher psychological reactance, and consequently, with higher avoidance behavior. Interestingly, for respondents experiencing IOEP as unexpected exposure, the association is reversed. First, a significant total effect exists (ß = −.164), indicating that higher frequency of unexpected exposure is associated with lower avoidance behavior. Second, this association is mediated by psychological reactance (ß = −.088), but no direct relation occurs. We are unable to prove that IOEP actually motivates these voters to search actively for campaign information, but this might be a plausible speculation. In any case, they behave differently from those who feel more threatened by incidentally received political information.

Table 3.

Completely standardized total, direct, and indirect effects of self-reported frequency of IOEP on information avoidance mediated by reactance.

	Total effect	Direct effect	Indirect effect
Respondents experiencing . . .
. . . IOEP as unexpected exposure (n = 456)	−.164 [−.261; −.068]**	−.076 [−.165; .008]	−.088 [−.160; −.020]**
. . . IOEP as unwanted exposure (n = 430)	.072 [−.038; .183]	−.086 [−.186; .014]	.157 [.071; .252]**

IOEP: incidental online exposure to political information.

Note. Bootstrap estimation with 1000 samples; 95-confidence interval in parentheses.

Two-tailed significance. *p < .05; **p < .01.

Finally, our fourth hypothesis is based on the initial interest of IOEP research. We assumed that a higher frequency of unexpected exposure may result in higher current campaign knowledge (H4a), whereas higher frequency of unwanted exposure will not result in greater campaign knowledge (H4b). For respondents experiencing IOEP as unexpected exposure, there is a significant direct association (ß = .088), and both total (ß = .105) and indirect connection (ß = .017) are significantly positive (Table 4), supporting H4a. For this group of voters, incidental exposure is positively associated to a higher campaign knowledge. In contrast, for respondents experiencing IOEP as unwanted exposure, higher frequency of unwanted exposure is not associated with knowledge gain. Thus, H4b is supported by the data as well. Furthermore, a small, but significant, negative indirect effect exists (ß = −.030), indicating that if unwanted exposure is related to a higher psychological reactance, knowledge might even decrease.

Table 4.

Completely standardized total, direct, and indirect effects of self-reported frequency of IOEP on current campaign knowledge mediated by reactance.

	Total effect	Direct effect	Indirect effect
Respondents experiencing . . .
. . . IOEP as unexpected exposure (n = 456)	.105 [.025; .187]*	.088 [.009; .169]*	.017 [.003; .040]**
. . . IOEP as unwanted exposure (n = 430)	−.011 [−.104; .076]	.018 [−.078; .113]	−.030 [−.057; −.010]**

IOEP: incidental online exposure to political information.

Note. Bootstrap estimation with 1000 samples; 95-confidence interval in parentheses.

Two-tailed significance. *p < .05; **p < .01.

Discussion and conclusions

Just a decade ago, the dominant assumption in communication research was that the Internet would contribute to strengthening democracy and revitalizing political communication. However, a high degree of skepticism lately has replaced this optimistic view, viewing the Internet as a severe threat to democratic politics and public communication. We advocate for a more balanced view on the Internet’s value in political communication that includes its potential, while simultaneously raising awareness of its dangers to democracy and a democratic public sphere.

This article contributes to developing such a view of incidental online exposure to political communication. Studies have shown that unintentional reception of public-affairs news can contribute to significant political knowledge acquisition due to “passive learning.” In this article’s theoretical section, we argued that this will apply above all to users who do not feel disturbed, as they usually bring along a minimum of interest or develop interest due to unforeseen contact. Thus, they might be accidentally informed, as Tewksbury et al. (2001) put it. Beyond that, citizens exist in every society who are not only uninterested, but also fundamentally unwilling to engage voluntarily in politics and elections. We have argued that they might perceive IOEP rather as a restriction of their behavioral freedom and might react to it with frustration and cognitive obstinacy.

Using the 2017 German national election as an example, we showed that IOEP is a common phenomenon in Germany. This means, first, that the virality of social online networks actually leads to many people coming into contact with the election campaign without explicitly wanting to do so. However, this is not unreservedly good news for election campaigners, as IOEP is actually associated to reactance among substantial segments of the electorate. Furthermore, reactance seems to encourage those concerned to actively avoid any further contact with information about the election, candidates, and parties. In other words, the online campaign’s viral character is correlated with the opposite of what was intended: Not all voters are attracted, some might even be repelled. Thus, IOEP might exacerbate existing problems with those who must be regarded as “problem children of democracy.”

Even before the online age, citizens constantly have been confronted with campaign communication during election campaigns without actively seeking them. For example, they are confronted with election posters in public spaces that attract unwanted attention, or face unrequested contact from election campaigners while walking through pedestrian zones. At this point, we only can speculate about the special quality of unwanted exposure to politics on the Internet. We suspect that this phenomenon is considered more invasive than other forms of unwanted contact because it is perceived as an infringement on a user’s personal space. Assuming that personal social online networks, the major playground for IOEP, are viewed as part of the personal sphere, for which special protection is claimed, then unwanted political messages’ intrusions into this space must be understood as being perceived as a strong “expectancy violation.” In this respect, IOEP is more comparable to the unsolicited election campaigner at one’s own house door than to contact in public spaces. An intrusion into one’s private sphere is probably understood as a more serious threat than restricted freedom of movement and action in public spaces and, thus, most likely results in reactance. Whether these assumptions are correct can be clarified further only by future research that might use expectancy-violation theory (Burgoon, 1978) as an analytical framework.

For further research on IOEP’s political effects, this means, first, that it is insufficient to ask about the frequency of such encounters, their preconditions, and about the medium in which they happen. More decisive than all the above are the political predispositions of those affected. This, in itself, is not a particularly surprising insight, but has not yet been considered in extant literature. As our data show, differential effects might be assumed, depending on the general information behavior of those affected and their attitude toward politics. To implement this, future research might want to distinguish carefully between currently unexpected and literally unwanted online exposure to political news. By presumably causing reactance for some while resulting in knowledge gain for others, IOEP probably widens the gap between the politically frustrated and those who seize new, sometimes involuntary, opportunities for political information offered by the Internet (for similar indications, see Heiss and Matthes, 2019). If we look at the mixed evidence that IOEP research has produced so far, the perspective on differential effects of IOEP can be very fruitful for a better understanding of consequences of unintended exposure to political information.

Our final consideration is how those affected will react to unrequested online exposure to the election campaign. According to our study, they may strive to avoid any further contact with election-related messages. The question for future research is whether and how this can succeed. All available findings—including our own—indicate that the use of social network sites and email portals sooner or later result in new confrontations with unpleasant content. The evil thing about inadvertent exposure is that it cannot be avoided easily precisely because it happens incidentally. The only safe way to avoid this would be to refrain from using these online media, at least during the height of an election campaign. However, this obviously would mean an even greater threat to one’s own freedom and, therefore, could be an even stronger reactance trigger. The feeling that one can regain one’s freedom of information only by restricting the freedom of one’s own media use could lead to a veritable spiral of reactance, with even more politically devastating consequences. Such effects are only to be expected in the long term and, therefore, lie outside this study’s scope. This brings us to some final remarks on limitations.

First, as has been mentioned often, cross-sectional survey data cannot be used for causal claims. A possibility of differing causal relationships always exists, even though the presented models were derived through theoretical means and showed acceptable fit for the predicted relationships. However, future research should use the presented results as an impetus for longitudinal and experimental studies to assess IOEP’s effects on psychological defense. After all, we can show that IOEP is perceived differently by different audiences, indicating that differential effects might exist as well.

Second, the measure of self-reported frequency of IOEP contains a particular inaccuracy. While the established and, in this study, applied operationalization aims at a subjective perception of how often one is confronted incidentally with political information, another measurement of actual IOEP situations would be interesting for comparison. Experimental studies like Bode (2016) or Lee and Kim (2017) can simulate different frequencies of inadvertent exposure, but these studies often lack external validity, as they cannot fully imitate various online user habits in real life. Combinations of observational data using log-file data with survey data or experience sampling methods may be an approach to get a more realistic look at the experiences and consequences of IOEP. The same applies to the measurement of the IEM as a resistance indicator. First, although Heiss et al. (2019) have shown that a lower motivation for political information is correlated with a higher value on the IEM scale (p. 13), it might be a suitable indicator but is not identical to the motivation. There might be further predictors of a high IEM than only the motivation. Further theoretical discussion and empirical evidence on the character of the IEM is required. And second, other reasons, such as political cynicism or a particular expectation of the Internet as a medium, could also lead to a lower political use of the Internet. The intention of this article was to use existing measures and to show that there are good reasons to expect differential effects of an unwanted and an unexpected exposure. With the help of a more explicit measure, which still needs to be developed and tested, it would be possible to work out the connection we have shown even more clearly. Further exploration of the resistance might also be helpful to better understand why someone does not like to be confronted with information on the election. It seems plausible to note a difference whether one individual is generally inclined to get informed and another just tries to avoid information on a particular election cause the preferred party or candidate seems to lose. Hence, both individual and situational characteristics should be included in further research.

Third, our study fails to specify more precisely what content led to the mentioned reactions. What we can say is that any information on the elections may lead to reactance and further avoidance. For our study, this was the main subject, as we argued that generally resisting political information is a crucial predictor of a perceived threat to free choice of information. Furthermore, what we cannot say is whether journalistic reporting on the elections, friends’ political expressions, or parties and politicians’ campaign communications created reactance or differed in their contributions to that effect. Some good reasons exist to believe that individually liking the communicator makes a difference on the reaction. For example, if the incidentally received information generally is sent by a disliked party, some reactance might occur, even though one is not resistant to political information in general due to dissonant feelings. Reactance may fail to appear when IOEP sources are at least accepted. Thus, some uncertainty exists due to the character of reactance inducing sources and contents, and we encourage future communication researchers to further examine IOEP and its effects on the online audience.

Supplemental Material

Appendix – Supplemental material for From incidental exposure to intentional avoidance: Psychological reactance to political communication during the 2017 German national election campaign

Supplemental material, Appendix for From incidental exposure to intentional avoidance: Psychological reactance to political communication during the 2017 German national election campaign by Frank Marcinkowski and Pero Došenović in New Media & Society

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Pero Došenović

Supplemental material

Supplemental material for this article is available online.

Notes

Author biographies

Frank Marcinkowski (PhD, Gerhard Mercator University of Duisburg, 1992) is full professor of Communication at the Department of Social Sciences, Heinrich Heine University Düsseldorf, Germany. His research interests include political communication, science communication, and public perception of digitization, artificial intelligence and its societal consequences.

Pero Došenović (MA, University of Muenster, 2016) is PhD student in Communication Studies and research assistant at the Department of Social Sciences, Heinrich Heine University Düsseldorf, Germany. His research interests include political online communication and participation, public perception of digitization and artifical intelligence as well as sport communication.

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